Cover Image

What Does it Mean for a Neural Network to Learn a "World Model"?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Kenneth, Viégas, Fernanda, Wattenberg, Martin
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Dialogue Action Tokens: Steering Language Models in Goal-Directed Dialogue with a Multi-Turn Planner
by: Li, Kenneth, et al.
Published: (2024)

When Bad Data Leads to Good Models
by: Li, Kenneth, et al.
Published: (2025)

Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task
by: Li, Kenneth, et al.
Published: (2022)

Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
by: Li, Kenneth, et al.
Published: (2023)

Measuring and Controlling Instruction (In)Stability in Language Model Dialogs
by: Li, Kenneth, et al.
Published: (2024)

Relational Composition in Neural Networks: A Survey and Call to Action
by: Wattenberg, Martin, et al.
Published: (2024)

Designing a Dashboard for Transparency and Control of Conversational AI
by: Chen, Yida, et al.
Published: (2024)

What Generative Artificial Intelligence Means for Terminological Definitions
by: Martín, Antonio San
Published: (2024)

What is Sentiment Meant to Mean to Language Models?
by: Burnham, Michael
Published: (2024)

Shared Global and Local Geometry of Language Model Embeddings
by: Lee, Andrew, et al.
Published: (2025)

The Geometry of Self-Verification in a Task-Specific Reasoning Model
by: Lee, Andrew, et al.
Published: (2025)

A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity
by: Lee, Andrew, et al.
Published: (2024)

Does Less Hallucination Mean Less Creativity? An Empirical Investigation in LLMs
by: Banerjee, Mohor, et al.
Published: (2025)

Can Interpretation Predict Behavior on Unseen Data?
by: Li, Victoria R., et al.
Published: (2025)

Does visualization help AI understand data?
by: Li, Victoria R., et al.
Published: (2025)

How to Connect Speech Foundation Models and Large Language Models? What Matters and What Does Not
by: Verdini, Francesco, et al.
Published: (2024)

ICLR: In-Context Learning of Representations
by: Park, Core Francisco, et al.
Published: (2024)

Assessing the Capability of LLMs in Solving POSCOMP Questions
by: Viegas, Cayo, et al.
Published: (2025)

Chronotome: Real-Time Topic Modeling for Streaming Embedding Spaces
by: Lim, Matte, et al.
Published: (2025)

Agent2World: Learning to Generate Symbolic World Models via Adaptive Multi-Agent Feedback
by: Hu, Mengkang, et al.
Published: (2025)

Large Language Models Generate Harmful Content Using a Distinct, Unified Mechanism
by: Orgad, Hadas, et al.
Published: (2026)

What is it for a Machine Learning Model to Have a Capability?
by: Harding, Jacqueline, et al.
Published: (2024)

Word Meanings in Transformer Language Models
by: Grindrod, Jumbly, et al.
Published: (2025)

Evaluating the World Model Implicit in a Generative Model
by: Vafa, Keyon, et al.
Published: (2024)

Enhancing Agent Learning through World Dynamics Modeling
by: Sun, Zhiyuan, et al.
Published: (2024)

What is a Number, That a Large Language Model May Know It?
by: Marjieh, Raja, et al.
Published: (2025)

Beyond the Rosetta Stone: Unification Forces in Generalization Dynamics
by: Blum, Carter, et al.
Published: (2025)

PatchWorld: Gradient-Free Optimization of Executable World Models
by: Bai, Jiaxin, et al.
Published: (2026)

WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment
by: Tang, Hao, et al.
Published: (2024)

Text2World: Benchmarking Large Language Models for Symbolic World Model Generation
by: Hu, Mengkang, et al.
Published: (2025)

Does ChatGPT Have a Mind?
by: Goldstein, Simon, et al.
Published: (2024)

How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translation
by: Meng, Yan, et al.
Published: (2024)

PragWorld: A Benchmark Evaluating LLMs' Local World Model under Minimal Linguistic Alterations and Conversational Dynamics
by: Vashistha, Sachin, et al.
Published: (2025)

Binary Neural Networks for Large Language Model: A Survey
by: Liu, Liangdong, et al.
Published: (2025)

Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks
by: Zhang, Yingji, et al.
Published: (2023)

Persian Pronoun Resolution: Leveraging Neural Networks and Language Models
by: Mohammadi, Hassan Haji, et al.
Published: (2024)

Decomposing the Delta: What Do Models Actually Learn from Preference Pairs?
by: Lee, Chia-Hsuan, et al.
Published: (2026)

Measuring Meaning Composition in the Human Brain with Composition Scores from Large Language Models
by: Gao, Changjiang, et al.
Published: (2024)

Unpacking Let Alone: Human-Scale Models Generalize to a Rare Construction in Form but not Meaning
by: Scivetti, Wesley, et al.
Published: (2025)

Decomposing Elements of Problem Solving: What "Math" Does RL Teach?
by: Qin, Tian, et al.
Published: (2025)